Animated Tornado Tree Rings

Why we love it

We love it when cartography makes sense of complicated data. This map delivers, showing thousands of tornados averaged over time and space to reveal seasonal and regional patterns. The legend uses colour to denote month, and size to show tornado intensity. We see the timing of peak tornado activity through animation. The rings, colour-coded by season, keep us fixed on the map instead of the legend. We learn that some places have year-round tornado risk, while others are limited to summer months. A looping animated GIF emphasise the fact that tornados occur as a cyclical, annual process.

Why it works

Animated data really makes this map work. It draws focus beyond individual circles to the aggregate effect of circles synchronising, blooming and changing in waves. Just as when we watch a flock of birds, we no longer see individual birds. We abstract them into a simpler, larger visual object—a flock. With animated maps, hundreds of disparate symbols become a single, organic shape that shows both location and rate of change. This map is a great example of how animation can clarify a complicated dataset and nuance our understanding of geographic processes playing out over space and time.

Important steps

Join features

Rather than map raw data, cartographers often use GIS to merge or join features to show larger patterns, calculating counts or averages and other statistical measures expressed by area, time or per capita.

Spatial aggregation with hexagons

There is no single best amount of spatial aggregation. Use ArcGIS Pro to experiment with the size of the hexagon mesh. Here, the optimal size was roughly metropolitan-city-sized areas that balanced fine detail with regional patterns.

Requirements

Data and software

Analysis

The historic tornado coordinates table was converted to lines via XY To Line geoprocessing tool. Lines and attributes were divided into 12 layers (one for each month), and fed into aggregation hexagons via spatial join for weighted tornado intensity value per cell.

Time

Parsing, spatial aggregation, symbology exploration, and layout creation took around half a day. Most of this time was in visual exploration of symbology options for the overlapping layers and ensuring consistent settings.

Tips and tricks

Show temporal context

To help smooth the animation and provide temporal context, each month is surrounded by slightly faded rings of the months just before and after. When we see large size difference in those context rings, we can infer things are changing quickly.

Experiment with spatial aggregation

When there is far too much data to map, such as thousands of tornado tracks, use spatial aggregation (also known as, binning) to count how many events occur in a standard area such as a square or hexagon.